The present invention relates to video signal measurement, and more particularly to a method of measuring the frequencies of a multiple sinusoidal burst signal in the presence of noise and other non-linear distortions.
In testing the frequency response of a video system, test signals with multiple bursts of sinusoids at various frequencies per line, i.e., “multi-burst” test signals, are used. Typically five or six bursts, each of a different frequency and amplitude, are used. The specific set of frequencies may vary depending on the video test signal source and video format. These frequencies are not always known a priori when making frequency response measurements. FIG. 1 is a normalized view of a typical multi-burst signal for a high definition video signal showing 2500 samples at a sample rate of 62.5 MHz.
Also devices for both broadcast digital video and computer video, such as set-top boxes and computer graphics cards, may reformat the video such that frequencies may shift. These devices often introduce errors as well, such as noise and frequency aliasing. These errors and digital compression artifacts interfere with prior methods of measuring burst frequencies.
One previous method for measuring video test sinusoidal burst frequencies is based on four consecutive sample points. This method assumes a perfect sinusoid and uses a set of four equations and four unknowns based on:
 f(t+n)=A*sin (ω*(t+n))+DC, n=0 to 4sin (ω*(t+n))=cos (ω*t)*sin (ω*n)+cos (ω*n)*sin (ω*t)                Solution:ω=2*PI*f=a cos (((f(n+3)−f(n))/(f(n+2)−f(n+1)) −1)/2)This method has the advantage of only requiring four sample points and a corresponding relatively small number of calculations. For signals very close to the ideal sinusoid, under the right sampling conditions and for most frequencies, accuracy of measurement is very good.        
It does however have several disadvantages. If f(n+2)=f(n+1), ω is indeterminate. This condition occurs for all samples in the case where ω=PI/2 and the sampling phase is an integer multiple of PI/4. This condition occurs for ideal sinusoids of any ω and phase such that the extremes of the sinusoid are sampled symmetrically. In the presence of noise, quantization error and other interference it is possible for this condition to occur for more frequencies, especially low and the highest frequencies. For zero mean error in f(t+n) the error in ω is not generally zero mean, which means that averaging is not very effective for removing random zero mean errors such as typical noise.
Other methods generally time window individual bursts and use either zero-crossings or fast Fourier Transform (FFT) type methods for finding one frequency, or time window a group of multiple bursts and find individual peaks in the frequency domain. The selection of the time window is either manual, set as an expected location on a video line in anticipation of a specific test signal, or automatic using envelope detection to find burst envelopes. Frequency measurement methods using zero-crossings are the most susceptible to errors due to noise and non-linear distortions. The FFT type methods are less susceptible to noise, but the methods for finding the peak frequencies do not inherently discriminate between burst fundamental frequencies and burst side-lobes due to time-domain windowing, non-linear distortions and noise, especially when one burst is greatly attenuated relative to bursts of nearby frequencies.
Other related methods of frequency component estimation, such as the MUSIC algorithm, signal identification methods such as Prony's method, etc. which approximate eigen vectors and/or poles and zeroes of a linear system are not desirable because they tend to be relatively computationally expensive and/or not particularly robust in the presence of burst side-lobes, correlated non-linear distortions and noise, as in MPEG decoded component analog video. These methods also require an additional step to discriminate between burst fundamental frequencies and side-lobe frequencies.
What is desired is an automated method of measuring the frequencies of sinusoidal test signal components, i.e., multi-burst frequencies, of impaired analog and digital video. In particular it is desired to have one measurement method that is robust in the presence of random noise, quantization error, compressed video impairments and other non-linear distortions and interference. It is desired for the measurement method to work with sinusoidal test signal components with various time windows—duration and envelop shape, burst spacing, etc. Further it is desired for the measurement method to work with different video standards—YPbPr, RGB, high definition, standard definition and computer video—as well as with variable sample rates, not necessarily known a priori or related to the clock rate of the corresponding digital video. Finally it is desired for the measurement method to have both good accuracy and computational efficiency, i.e., relatively low processing for a given accuracy, especially for higher frequencies.